研究目的
Comparing the performances of a miniaturized NIR spectrometer (JDSU MicroNIR 2200) with a conventional bench-top NIR spectrometer (Foss NIRSystems 5000) for predicting soil carbon and nitrogen contents in laboratory conditions on Malagasy soils.
研究成果
The JDSU MicroNIR 2200, despite its narrower spectral range and lower resolution, provided nearly as accurate predictions for soil C and N as the conventional Foss spectrometer after appropriate pretreatments and bias correction. It required fewer latent variables and was less prone to overfitting, making it a cost-effective alternative for laboratory use. Future work should test its performance in field conditions.
研究不足
The study was conducted only in laboratory conditions; field performance was not tested. The validation set had samples richer in C and N than the calibration set, leading to extrapolation issues. MicroNIR spectra required manual reference scanning and pretreatments, which could be tedious. The spectral range of MicroNIR is narrower, potentially limiting its applicability for other soil properties.
1:Experimental Design and Method Selection:
The study used a comparative design with two spectrophotometers to measure soil reflectance. Modified partial least squares (PLS) regression was employed for calibration and prediction models, with mathematical pretreatments like SNV, detrending, and MSC to enhance spectral data.
2:Sample Selection and Data Sources:
360 topsoil samples from eight sites in Madagascar, collected at 0-5 cm depth, air-dried, sieved, and ground. Conventional C and N analyses were done using an Elemental Analyzer CHN Carlo Erba NA
3:List of Experimental Equipment and Materials:
20 JDSU MicroNIR 2200 spectrophotometer, Foss NIRSystems 5000 spectrophotometer, Elemental Analyzer CHN Carlo Erba NA 2000, ring cups, Petri dishes, Spectralon disk for white reference.
4:Experimental Procedures and Operational Workflow:
Reflectance measurements were taken on oven-dried samples using both spectrometers. For Foss, automatic scanning with internal reference; for MicroNIR, manual scanning with external white and black references. Spectra were recorded in absorbance, and data analyzed using WinISI IV and R software with pls package.
5:Data Analysis Methods:
PLS regression with cross-validation (Venetian blinds method) to determine optimal latent variables. Performance evaluated using SEC, SECV, SEP, SEPc, R2, and RPD. Mathematical pretreatments applied to reduce noise and enhance features.
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